An Interview with 2019 Tanaka Dissertation Award Winner Felix Cheung
1. In a nutshell, what was your dissertation about?
My dissertation looks at the role of income redistribution (i.e., governmental efforts to reduce income disparity) on life satisfaction. Income inequality is an important social issue, and former US president Barack Obama called it "the defining challenge of our time." Increasing attention has been paid to the study of income inequality within psychology. In my dissertation, I aimed to advance this area of research by focusing on solutions to income inequality (instead of income inequality per se). That's why I chose to study income redistribution.
Based on 30 years of German data and 24 years of international data, I found that increases in income redistribution were associated with greater life satisfaction. More importantly, this link was positive across individual, national, and cultural characteristics. For instance, income redistribution predicted greater well-being for the poor and the rich and for liberals and conservatives. Therefore, my dissertation showed that income redistribution does not simply redistribute happiness. Rather, it is linked to improvement in population well-being.
2. What are you working on right now, and what do you want to do in the future?
My overall research program examines the population determinants and consequences of a satisfying, purposeful, and engaging life. My on-going research continues to disentangle issues related to income disparity by incorporating the broader socioeconomic context (e.g., income mobility, GDP, poverty). In addition, I have expanded my research to consider the role of sociopolitical environment (e.g., Syria Conflict, the 2014 Hong Kong Occupy Central Movement, the Trump presidency, terrorist attacks).
A related line of research is a case study of Hong Kong. Hong Kong people live the longest life and enjoy one of the highest GDP per capita in the world. Both life expectancy and GDP are major policy indicators that are pursued in many, if not all, societies across the globe. If traditional policy indicators are aligned with citizens' well-being, then we have every reason to expect Hong Kong people to live one of the most satisfying, purposeful, and engaging life around the world. Yet, based on data from the Gallup World Poll, life satisfaction and life engagement in Hong Kong are the poorest among similarly developed societies. Hong Kong also has the lowest level of purpose in life among over 130 societies surveyed by Gallup. The case study of Hong Kong begs the question: Is a long and prosperous but dissatisfying and purposeless life a good life?
My long-term career goal is to formulate evidence-based well-being interventions to promote population well-being.
3. What research or statistical methods are you most excited to see pursued in our field in the coming years?
Causal inference.
The study of personality psychology relies substantially on observational data. Although drawing causal inference from observational data is always tricky, it does not mean that our causal inference cannot be improved. I would love to see our field continues to expand on our methodological and statistical toolbox. At a conceptual level, I think directed acyclic graph (DAG) has been gaining popularity across fields in guiding the selection of confounders and mediators. At a methodological level, I would love to see more applications of Mendelian randomization, regression discontinuity design, and natural experiments. At a statistical level, analytical techniques, such as convergent cross-mapping (a method that promises to distinguish causality from correlation with time-series data), instrumental variable analysis, and E-value, may be useful.
Of course, proper causal inference can only be achieved via proper research practices, and I believe recent advancements, such as registered report, open science, and increases in statistical power, are also incredibly important.
4. Do you have any advice for grad students? What was the best advice you got and helped you?
Trust yourself, but trust your data more.
When I was in the early stage of graduate school, I signed up to give a Brown Bag talk. I put together a cross-national study on income inequality and life satisfaction. I found, to my surprise at the time, that higher income inequality was linked to greater life satisfaction. I presented the results and, because the results contradicted my prior belief, I told the audience that I probably did something wrong. In other words, back then, I did not really trust the data or myself, and I did not adjust my belief despite contradicting evidence.
After the talk, a faculty member told me that he did not see any clear flaw inherent in the study itself and encouraged me to keep an open mind about the link between income inequality and life satisfaction. Eventually, a paper using the same dataset was published by another research group and made the same claim that income inequality may be linked to greater well-being. So, now, I have reasons to believe that I did not actually do anything wrong in that study (well, maybe except for getting scooped).
I have since read up on more existing studies on income inequality in other sister disciplines (e.g., economics, sociology, public health) and conducted additional studies on this topic. I am now convinced by the totality of evidence that income inequality (as defined and measured by Gini) can have both positive and negative consequences.